Merge pull request #35 from dcferreira/master

Upgraded papers from v2.3.0 to v3.0.0
CN-TU · Nov 12, 2018 · 7f45761 · 7f45761
2 parents 9312ead + 8c8ec94
commit 7f45761
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diff --git a/v2_papers/2003/mahoney_learning.json b/v2_papers/2003/mahoney_learning.json
@@ -1,5 +1,5 @@
 {
-  "version": "v2.3.0",
+  "version": "v3.0.0",
   "reference": {
     "title": "Learning rules for anomaly detection of hostile network traffic",
     "authors": [
@@ -20,8 +20,8 @@
     },
     "access_open": false,
     "curated_by": "vormayr, g.",
-    "curated_last_revision": "06-06-2018",
-    "curated_revision_number": 3
+    "curated_last_revision": "12-11-2018",
+    "curated_revision_number": 4
   },
   "data": {
     "datasets": [
@@ -359,11 +359,11 @@
     "tools": "missing",
     "algorithms": [
       {
-        "name": "LERAD",
-        "subname": "Learning Rules for Anomaly Detection",
+        "family": "rule_extraction",
+        "detail": "LERAD",
         "learning": "unsupervised",
         "role": "main",
-        "type": "heuristics",
+        "type": "classification",
         "metric/decision_criteria": "probabilistic",
         "source": "own_proposed",
         "parameters_provided": false
@@ -392,7 +392,7 @@
     ]
   },
   "result": {
-    "main_goal": "anomaly_detection",
+    "main_goal": "detect_attacks",
     "subgoals": [
       "anomaly_detection"
     ],

diff --git a/v2_papers/2004/wang_anomalous.json b/v2_papers/2004/wang_anomalous.json
@@ -1,5 +1,5 @@
 {
-  "version": "v2.3.0",
+  "version": "v3.0.0",
   "reference": {
     "title": "Anomalous Payload-based Network Intrusion Detection",
     "authors": [
@@ -18,8 +18,8 @@
     },
     "access_open": false,
     "curated_by": "bachl, m.",
-    "curated_last_revision": "06-06-2018",
-    "curated_revision_number": 3
+    "curated_last_revision": "12-11-2018",
+    "curated_revision_number": 4
   },
   "data": {
     "datasets": [
@@ -189,14 +189,14 @@
     "tools": "missing",
     "algorithms": [
       {
-        "name": "PAYL",
+        "family": "signature",
+        "detail": "payload-based anomaly detection",
         "learning": "unsupervised",
         "role": "main",
-        "type": "anomaly_detection",
+        "type": "specific_detection",
         "metric/decision_criteria": "mahalanobis",
         "source": "own_proposed",
-        "parameters_provided": false,
-        "subname": "payload-based anomaly detection"
+        "parameters_provided": false
       }
     ]
   },
@@ -247,7 +247,7 @@
     ]
   },
   "result": {
-    "main_goal": "anomaly_detection",
+    "main_goal": "detect_attacks",
     "subgoals": [
       "attack_classification"
     ],

diff --git a/v2_papers/2005/lakhina_mining.json b/v2_papers/2005/lakhina_mining.json
@@ -1,5 +1,5 @@
 {
-  "version": "v2.3.0",
+  "version": "v3.0.0",
   "reference": {
     "title": "Mining Anomalies Using Traffic Feature Distributions",
     "authors": [
@@ -22,8 +22,8 @@
     },
     "access_open": false,
     "curated_by": "maloku, b.",
-    "curated_last_revision": "06-06-2018",
-    "curated_revision_number": 3
+    "curated_last_revision": "12-11-2018",
+    "curated_revision_number": 4
   },
   "data": {
     "datasets": [
@@ -216,18 +216,18 @@
     "tools": "missing",
     "algorithms": [
       {
-        "name": "multiway subspace method",
-        "subname": "multiway subspace method",
+        "family": "_multiway_space_transformation",
+        "detail": "multiway subspace method",
         "learning": "unsupervised",
         "role": "main",
-        "type": "anomaly_detection",
+        "type": "space_transformation",
         "metric/decision_criteria": "euclidean",
         "source": "referenced",
         "parameters_provided": false
       },
       {
-        "name": "Hierarchical Agglomerative Clustering",
-        "subname": "hierarchical agglomerative",
+        "family": "hierarchical_clustering",
+        "detail": "agglomerative",
         "learning": "unsupervised",
         "role": "main",
         "type": "clustering",
@@ -236,8 +236,8 @@
         "parameters_provided": false
       },
       {
-        "name": "K-means",
-        "subname": "k-means",
+        "family": "kmeans_clustering",
+        "detail": "none",
         "learning": "unsupervised",
         "role": "competitor",
         "type": "clustering",
@@ -285,7 +285,7 @@
     ]
   },
   "result": {
-    "main_goal": "anomaly_detection",
+    "main_goal": "detect_anomalies",
     "subgoals": [
       "anomaly_detection",
       "attack_classification"

diff --git a/v2_papers/2005/moore_internet.json b/v2_papers/2005/moore_internet.json
@@ -1,5 +1,5 @@
 {
-  "version": "v2.3.0",
+  "version": "v3.0.0",
   "reference": {
     "title": "Internet Traffic Classification Using Bayesian Analysis Techniques",
     "authors": [
@@ -21,8 +21,8 @@
     },
     "access_open": false,
     "curated_by": "maloku, b.",
-    "curated_last_revision": "06-06-2018",
-    "curated_revision_number": 3
+    "curated_last_revision": "12-11-2018",
+    "curated_revision_number": 5
   },
   "data": {
     "datasets": [
@@ -96,8 +96,7 @@
     "feature_selections": [
       {
         "name": "Fast Correlation-Based Filter (FCBF)",
-        "type": "wrapper",
-        "classifier": "Naive Bayes",
+        "type": "filter",
         "role": "main"
       }
     ],
@@ -320,8 +319,8 @@
     "tools": "missing",
     "algorithms": [
       {
-        "name": "Naive Bayes",
-        "subname": "Naïve Bayes",
+        "family": "bayesian",
+        "detail": "naive bayes",
         "learning": "supervised",
         "role": "competitor",
         "type": "classification",
@@ -337,11 +336,11 @@
         "parameters_provided": false
       },
       {
-        "name": "Naı̈ve Bayes with kernel density estimation",
-        "subname": "Naı̈ve Bayes with kernel density estimation",
+        "family": "bayesian",
+        "detail": "Naive Bayes with Kernel Estimation Method",
         "learning": "supervised",
         "role": "competitor",
-        "type": "statistics",
+        "type": "classification",
         "metric/decision_criteria": "euclidean",
         "tools": [
           {
@@ -354,8 +353,8 @@
         "parameters_provided": false
       },
       {
-        "name": "Naive Bayes",
-        "subname": "Naïve Bayes with FCBF prefiltering",
+        "family": "bayesian",
+        "detail": "Naive Bayes with FCBF prefiltering",
         "learning": "supervised",
         "role": "competitor",
         "type": "classification",
@@ -371,11 +370,11 @@
         "parameters_provided": false
       },
       {
-        "name": "Naı̈ve Bayes with kernel density estimation and FCBF prefiltering",
-        "subname": "Naı̈ve Bayes with kernel density estimation and FCBF prefiltering",
+        "family": "bayesian",
+        "detail": "Naive Bayes with Kernel Estimation Method and FCBF prefiltering",
         "learning": "supervised",
         "role": "main",
-        "type": "statistics",
+        "type": "classification",
         "metric/decision_criteria": "euclidean",
         "tools": [
           {
@@ -427,7 +426,7 @@
     ]
   },
   "result": {
-    "main_goal": "traffic_classification",
+    "main_goal": "classify_traffic",
     "subgoals": [
       "traffic_classification"
     ],

diff --git a/v2_papers/2006/williams_apreliminary.json b/v2_papers/2006/williams_apreliminary.json
@@ -1,5 +1,5 @@
 {
-  "version": "v2.3.0",
+  "version": "v3.0.0",
   "reference": {
     "title": "a preliminary performance comparison of five machine learning algorithms for practical ip traffic flow classification",
     "authors": [
@@ -22,8 +22,8 @@
     },
     "access_open": false,
     "curated_by": "ferreira, d.",
-    "curated_last_revision": "06-06-2018",
-    "curated_revision_number": 4
+    "curated_last_revision": "12-11-2018",
+    "curated_revision_number": 5
   },
   "data": {
     "datasets": [
@@ -606,8 +606,8 @@
     ],
     "algorithms": [
       {
-        "name": "Naive Bayes",
-        "subname": "naive bayes with discretization",
+        "family": "bayesian",
+        "detail": "naive bayes with discretization",
         "learning": "supervised",
         "role": "main",
         "type": "classification",
@@ -623,8 +623,8 @@
         "parameters_provided": false
       },
       {
-        "name": "Naive Bayes",
-        "subname": "naive bayes with kernel density estimation",
+        "family": "bayesian",
+        "detail": "Naive Bayes with Kernel Estimation Method",
         "learning": "supervised",
         "role": "main",
         "type": "classification",
@@ -640,8 +640,8 @@
         "parameters_provided": false
       },
       {
-        "name": "Decision Tree",
-        "subname": "c4.5 decision tree",
+        "family": "decision_tree",
+        "detail": "C4.5",
         "learning": "supervised",
         "role": "main",
         "type": "classification",
@@ -657,10 +657,11 @@
         "parameters_provided": false
       },
       {
-        "name": "Bayesian Network",
+        "family": "bayesian",
+        "detail": "bayesian network",
         "learning": "supervised",
         "role": "main",
-        "type": "classification",
+        "type": "modeling",
         "metric/decision_criteria": "probabilistic",
         "tools": [
           {
@@ -673,8 +674,8 @@
         "parameters_provided": false
       },
       {
-        "name": "Decision Tree",
-        "subname": "naive bayes decision tree",
+        "family": "decision_tree",
+        "detail": "naive bayes decision tree",
         "learning": "supervised",
         "role": "main",
         "type": "classification",
@@ -721,7 +722,7 @@
     ]
   },
   "result": {
-    "main_goal": "traffic_classification",
+    "main_goal": "classify_traffic",
     "subgoals": [
       "traffic_classification"
     ],

diff --git a/v2_papers/2006/wright_oninferring.json b/v2_papers/2006/wright_oninferring.json
@@ -1,5 +1,5 @@
 {
-  "version": "v2.3.0",
+  "version": "v3.0.0",
   "reference": {
     "title": "on inferring application protocol behaviors in encrypted network traffic",
     "authors": [
@@ -19,8 +19,8 @@
     },
     "access_open": true,
     "curated_by": "vormayr, g.",
-    "curated_last_revision": "06-06-2018",
-    "curated_revision_number": 7
+    "curated_last_revision": "12-11-2018",
+    "curated_revision_number": 8
   },
   "data": {
     "datasets": [
@@ -128,34 +128,38 @@
     "tools": "missing",
     "algorithms": [
       {
-        "name": "Hidden Markov Model (HMM)",
+        "family": "markov_process",
+        "detail": "Hidden Markov Model (HMM)",
         "learning": "supervised",
         "role": "main",
-        "type": "classification",
+        "type": "modeling",
         "metric/decision_criteria": "probabilistic",
         "source": "referenced",
         "parameters_provided": false
       },
       {
-        "name": "viterbi",
+        "family": "markov_process",
+        "detail": "viterbi",
         "learning": "supervised",
         "role": "main",
-        "type": "classification",
+        "type": "modeling",
         "metric/decision_criteria": "probabilistic",
         "source": "referenced",
         "parameters_provided": false
       },
       {
-        "name": "K-nearest Neighbors (KNN)",
+        "family": "knn",
+        "detail": "none",
         "learning": "semisupervised",
         "role": "main",
-        "type": "clustering",
+        "type": "classification",
         "metric/decision_criteria": "mutual_information",
         "source": "referenced",
         "parameters_provided": false
       },
       {
-        "name": "vector quantization",
+        "family": "_vector_quantization",
+        "detail": "vector quantization",
         "learning": "semisupervised",
         "role": "main",
         "type": "clustering",
@@ -189,7 +193,7 @@
     ]
   },
   "result": {
-    "main_goal": "traffic_classification",
+    "main_goal": "classify_traffic",
     "subgoals": [
       "application_classification",
       "classification_of_encrypted_traffic",